用大模型识别引力波，仅90个样本就达97.4%准确率。
Large Language Models for Limited Noisy Data: A Gravitational Wave Identification Study
- 用微调的大模型直接从观测数据中提取信号特征
- 仅90个真实事件样本即达97.4%识别准确率
- 适合噪声复杂、标注数据少的天文观测任务
本研究探讨大语言模型（LLMs）在非高斯、非平稳噪声且标注样本有限的天体数据处理中是否优于传统神经网络。引力波观测为此提供了理想测试场景：仅使用90个LIGO事件，微调后的LLM即可实现97.4%的信号识别准确率。实验表明，与依赖大规模模拟数据的传统网络不同，额外增加模拟样本无法提升LLM性能；而规模扩展实验显示，模型和数据量增大时性能可预测地提升。结果表明，LLM能直接从观测数据中提取判别性结构，为引力波识别提供高效方案。该策略可能推广至具有类似噪声特性的其他天文领域，如射电或脉冲星观测。
原文摘要 · Abstract (English)
This work investigates whether large language models (LLMs) offer advantages over traditional neural networks for astronomical data processing, in regimes with non-Gaussian, non-stationary noise and limited labeled samples. Gravitational wave observations provide an suitable test case, using only 90 LIGO events, finetuned LLMs achieve 97.4\% accuracy for identifying signals. Further experiments show that, in contrast to traditional networks that rely on large simulated datasets, additional simulated samples do not improve LLM performance, while scaling studies reveal predictable gains with increasing model size and dataset size. These results indicate that LLMs can extract discriminative structure directly from observational data and provide an efficient assessment for gravitational wave identification. The same strategy may extend to other astronomical domains with similar noise properties, such as radio or pulsar observations.
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